Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding

Hugging Face Daily Papers Papers
large-language-models language-representation schema-design knowledge-activation llm-research

Summary

This paper argues that designing advanced language representations to shape cognitive schemas is a key frontier for expanding LLM intelligence without scaling parameters. It provides formalizations and empirical evidence showing that different linguistic structures significantly impact model performance and internal feature activations.

Although natural language is the default medium for Large Language Models (LLMs), its limited expressive capacity creates a profound bottleneck for complex problem-solving. While recent advancements in AI have relied heavily on scaling, merely internalizing knowledge does not guarantee its effective application. Defining language representation as the linguistic and symbolic constructs used to map and model the real world, this paper argues that shaping schemas through advanced language representation is the next frontier for expanding LLM intelligence. We posit that an LLM's knowledge activation and organization -- its schema -- depends heavily on the structural and symbolic sophistication of the language used to represent a given task. This paper contributes both a formalization of this claim and the empirical evidence to support it. With a new formalization, we present multiple lines of evidence to support our position: Firstly, we review recent empirical practices and emerging methodologies that demonstrate the substantial performance gains achievable through deliberate language representation design, even without modifying model parameters or scale. Secondly, we conduct controlled experiments showing that LLM performance and its internal feature activations vary under different language representations of the same underlying task. Together, these findings highlight language representation design as a promising direction for future research.
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Source: https://huggingface.co/papers/2605.09271

Abstract

Language representation design significantly impacts large language model performance and internal feature activations, offering a promising research direction for enhancing model intelligence without scaling or parameter modifications.

Although natural language is the default medium forLarge Language Models(LLMs), its limited expressive capacity creates a profound bottleneck for complex problem-solving. While recent advancements in AI have relied heavily on scaling, merely internalizing knowledge does not guarantee its effective application. Defininglanguage representationas the linguistic and symbolic constructs used to map and model the real world, this paper argues that shapingschemas through advancedlanguage representationis the next frontier for expanding LLM intelligence. We posit that an LLM’sknowledge activationand organization -- itsschema-- depends heavily on the structural and symbolic sophistication of the language used to represent a given task. This paper contributes both a formalization of this claim and the empirical evidence to support it. With a new formalization, we present multiple lines of evidence to support our position: Firstly, we review recentempirical practicesandemerging methodologiesthat demonstrate the substantial performance gains achievable through deliberatelanguage representationdesign, even without modifying model parameters or scale. Secondly, we conductcontrolled experimentsshowing that LLM performance and itsinternal feature activationsvary under differentlanguage representations of the same underlying task. Together, these findings highlightlanguage representationdesign as a promising direction for future research.

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